Silviase/jgov_v1.4_full_ann huggingface dataset:
each firm exports to, we can also define NUM_EXPORTER and BANKINFO by region. We assume that BANKINFO measured regardless of destination regions is a proxy for information held by banks on foreign markets in general, while BANKINFO measured for each destination region is a proxy for region-specific information held by banks. For each firm, we use the BANKINFO variable in order to capture the amount of information provided by the main bank. In order to control for the size of the main bank, we also include NUM_CLIENT in our explanatory variables. $ ^{16} $ To illustrate the distribution of NUM_CLIENT and BANKINFO, we tabulate the numbers of these two variables for the sample banks in Table 2.

One might argue that firms which are thinking of expanding their business overseas might try to establish a transaction relationship with a bank which is more likely to have a lot of overseas information. Given that such reverse causality could generate simultaneous equation bias in our estimation, we limit the sample to firms who had the same main bank throughout year t-3 to year t. This allows us to focus on firm-bank pairs where the relationship is independent of the firm's decision to start exporting in year t. $ ^{17} $

As for firm-specific variables, we include variables representing firms' size, labor quality, financial constraints, own overseas activities, and productivity. For firm size we use the (logarithm of) the number of employees ( $ LN\_NUMWORKER $ ) and for labor quality the average wage (WAGE). Regarding financial constraints, we construct a number of variables: the leverage of a firm (ratio of total liabilities to total assets, FLEV), the ratio of bank loans to total liabilities (FBDEP), the ratio of liquidity assets to liquidity liabilities (FLIQ), and the short-term loan ratio (ratio of short-term bank borrowing to total bank borrowing, STLOAN). We construct a number of variables representing firms' own overseas activities: the share of overseas establishments (FOR_BRANCH), measured as the ratio of a firm's number of overseas branches or offices (not including overseas subsidiaries or affiliates) to the firm's total number of establishments, branches, or offices, including both domestic and overseas ones; the share of overseas employees (FOR_EMP), measured as the ratio of a firm's number of workers employed in overseas branches or offices (not including overseas subsidiaries or affiliates) to the firm's total number of workers employed in all establishments, branches, or offices; the overseas investment share (FOR_INV), measured as the ratio of a firm's overseas investment, including portfolio investment, to the firm's
total investment; and the overseas lending share $ (FOR\_LOAN) $ , measured as the ratio of a firm’s lending to affiliated firms overseas to the firm’s total lending to affiliated firms at home and abroad. $ ^{18} $

As for firm productivity, which, as mentioned above, is widely considered to be an important determinant of the export decision, we use the firm-level TFP data provided in the East Asian Listed Companies Database (EALC) 2010. $ ^{19} $ The firm-level TFP in the database is calculated using the multilateral TFP index method developed by Good et al. (1997). $ ^{20} $ Details on the TFP measure are provided in the Appendix.

## I NSERT Tables 1 & 2

Our firm-bank matched data cover the period from fiscal 1997 to 2008. In order to control for the potential influence of outliers, we excluded observations in the tails for each variable. $ ^{21} $ Table 3 presents the correlation matrix for the variables used in our empirical analysis, while Table 4 shows the distribution of our sample firms by industry and year. As can be seen from Table 4, sample firms are concentrated in a limited number of industries (e.g., food and kindred products, chemicals, non-electrical machinery, electrical and electronic machinery, motor vehicles, transportation equipment and ordnance).

## I NSERT Tables 3 & 4

### 4. Estimation Results

### 4.1 The decision to enter specific markets

We first examine the determinants of firms' decision to participate in a new export market by estimating Eq. (1). The estimation is conducted using observations for firms which did not export during the years t-3 to t ("never" exporters) and observations for firms which did not export during the years t-3 to t-1 but exported in year t (first-time exporters). Thus, observations for firms which exported in at least one year during t-3 to t-1 as well as t are excluded in the estimation. The results of the random effect probit estimation (average marginal effects) and the
panel logit estimation (odds ratios) are shown in Tables 5 and 6, respectively. The first two columns in Table 5 show the results when we use $ NEW\_EXP $ as the dependent variable and including (Column (1)) or excluding (Column (2)) $ TFP \times BANKINFO $ among the explanatory variables. Columns (3) and (4) repeat the same regressions but using $ NEW\_EXP\_REGION $ as the dependent variable. In Columns (1) to (4) in Table 5, we do not distinguish between destination regions and the BANKINFO variable is simply the ratio of the number of a firm's main bank's exporting clients – regardless of the destination region – to the total number of the bank's client firms. BANKINFO here therefore captures the main bank's general exposure (not specific to a destination region) to client firms with export activities. $ ^{22} $ The same applies to Columns (1) to (3) in Table 6. However, in the last column of Tables 5 and 6, we use the region-specific BANKINFO variable corresponding to the region to which a firm starts exporting. $ ^{23} $ In the case where a firm starts exporting to more than one region at a time, we randomly assign the region-specific BANKINFO. Finally, it should be noted that Columns (1) to (3) in Table 6 show the results using the same variables but different models for the panel logit estimation; that is, a population average model (PA), a fixed effect model (FE), and a random effect model (RE).

Looking at the results shown in Table 5 and focusing on the variable of main interest, BANKINFO, we find that the coefficient is positive and significant in all estimations. Similarly, Table 6, which shows the results based on the panel logit estimation, suggests that main banks with greater exposure to firms with overseas business raise the likelihood that their client firms start exporting, hinting at the presence of information spillovers from the main bank, which is consistent with our prediction. Further, the results in Column (4) in Table 6 suggest that when we take account of destination region-specific information, BANKINFO has a significant positive effect on firms' export decision even when we control for firm-specific fixed effects. $ ^{24} $

As for the other explanatory variables, firms’ own overseas activities (e.g., the overseas employee ratio) have a positive effect on firms’ decision to start exporting in many of the cases. On the other hand, for firm size, leverage, and liquidity the results vary depending on the estimation procedure and these variables are associated with a higher probability of starting
exporting only in some cases.

A notable result is that the TFP level has almost no impact on the export decision. Given that the correlation between TFP and the interaction term between TFP and BANKINFO (TFPxBANKINFO) is very high for the whole sample, we run the same regressions without the interaction term (i.e., Columns (2) and (4) in Table 5). The results remain qualitatively unchanged. This result is consistent with the finding in previous studies such as Todo (2011) that TFP is not a sufficiently strong factor to explain the export decision of Japanese firms.

Next, in order to examine whether the effect of region-specific information spillovers differs depending on the destination region we split the sample by export destination region. The estimation results for the sub-samples by destination region are shown in Table 7. The results suggest that BANKINFO has a significant positive effect on firms' export decision when they start exporting to Asia (Column (1)), but that this is not the case for other regions. These results may reflect the fact that most Japanese banks have been increasingly putting efforts into their business in Asia by expanding service networks there while restructuring services in other regions, particularly in developed regions. Furthermore, because first-time exporters to Asia tend to be smaller firms than those to other regions, the result may imply that information accumulated in main banks is more important for smaller firms, which do not have adequate capabilities to collect overseas information by themselves. This line of reasoning is supported by the fact that in Table 7 firm size has a significantly positive effect on the export decision in all cases except Asia.

### 4.2 Export volume and export growth

Table 8 reports the fixed-effect panel estimation results of Eq. (2). In the estimation, we only include observations of first-time exporters, and we examine whether information spillovers through main banks affect the export volume (the value of exports in logarithm) or the growth rate of exports from year t to year $ t+1 $ after the firm started exporting. Beginning with the results in Panel (a) of Table 8, we find that the coefficient on BANKINFO is not significant, implying that information spillovers do not have a clear effect on the volume of exports (i.e., the intensive margin). While firms' own international activities (the overseas investment ratio in Column (1)) tend to have a positive effect on the intensive margin, most of the other explanatory variables do not have a significant coefficient. Although it is possible that the results partly reflect the small sample size, they suggest that the export volume is mainly explained by firm fixed effects.

Next, we further split the sample by destination region and estimate the same equations as in Panel (a) for each destination region. Panel (b) shows the estimated coefficient on BANKINFO for each destination region. As can be seen, the coefficient is not significant in most cases and the impact of BANKINFO is ambiguous. Although we find a negative and significant
coefficient on BANKINFO for the cases of North America, Africa, and Oceania, we should note that the number of observations is small, particularly in the latter two cases, for which we could not calculate F-values. Therefore, we do not obtain clear and robust results for the impact of information spillovers on the intensive margin. This is in line with Koenig et al. (2010), who also do not find a significant impact of export spillovers on the intensive margin. Although our results are consistent with their results, which factors affect the intensive margin of exports is an issue that deserves further scrutiny.

### 4.3 Robustness checks

As a robustness check, we also estimated Eq. (1) using a logit estimator, for which the standard errors are corrected for clustering. Taking into account that observations within the same firm are not independent, standard errors are corrected for clustering across firms. Alternatively, standard errors are corrected for clustering across main banks, taking into consideration the possibility that observations of firms which have a transaction relationship with the same bank are not independent. In both cases, the logit estimation results with clustered standard errors are consistent with the results in Table 5 and BANKINFO has a significant positive effect on firms' export decision. $ ^{25} $

In addition, bank characteristics may affect firms' export decision. For example, the Japan Bank for International Corporation (JBIC, what used to be the Export-Import Bank of Japan) is a government financial institution which was originally established to promote cross-border trade and foreign investment. Therefore, JBIC may be particularly active in helping firms to start exporting. On the other hand, major commercial banks may differ from regional banks or local banks in terms of their scope of business and hence in the characteristics of information accumulated by them. In order to control for differences in bank characteristics, we include a JBIC dummy and a dummy for major commercial banks in the export decision estimation. However, neither dummy variable has a significant coefficient, and including these dummy variables does not change the significance of the BANKINFO variable.

Finally, there may be several alternative ways to measure the amount of information on export markets available to a firm. While our main variable, BANKINFO, measures the intensity of banks' exposure to exporting firms, the absolute number of a bank's export client firms, NUM_EXPORTER may be a better way to measure the amount of information on export markets. In the first and second columns in Table 9, we show the estimation results based on the same model as Equation (2) in Table 5 using either NUM_EXPORT or $ LN\_NUM\_EXPORT $ (the natural
logarithm of $ NUM\_EXPORT $ instead of BANKINFO. Taking account of the high correlation between these two variables and $ NUM\_CLIENT $ , we exclude $ NUM\_CLIENT $ from our explanatory variables. The results imply that firms whose main bank has transaction relationships with a larger number of exporters are more likely to start exporting. On the other hand, $ NUM\_CLIENT $ does not have a significant coefficient in the third column in Table 9. These results suggest that main banks with a larger number of exporting client firms more likely provide non-exporting client firms effective information on foreign markets and positively affect their export decision.

We also conduct two additional estimations for the robustness check using sub-samples of our sample firms. First, due to the manner of calculating BANKINFO, we might wrongly measure each bank’s exposure to export market information when the number of client firms for a bank is very small. In fact, some of the regional banks have a limited number of listed companies as their clients. In such a case, BANKINFO might not work as a good proxy for the information related to export markets. In Table 10, we limit our sample firms to firms whose main bank has more than 30 (first and third columns) or 50 (second and fourth columns) client firms (NUM_CLIENT). The results are consistent with those presented in Table 5, which confirms the robustness of our results. Second, BANKINFO could largely vary due to mergers and acquisitions (M&As) among banks. In fact, a certain amount of variation in BANKINFO in the 2000s is driven by M&As among banks. In order to exclude the effect of banks’ M&As and check the robustness of our results, we repeat the same estimations as those in Table 5 excluding the sample firms whose main banks experienced M&As. The results summarized in Table 11 show the consistent results with our baseline estimations. $ ^{26} $

### 5. Concluding Remarks

In this paper, we examined whether information spillovers through main banks affect client firms' export behavior (i.e., the extensive and intensive margins). We find that information spillovers through main banks have a positive effect on client firms' decision to start exporting.
This implies that information on destination markets provided by main banks substantially reduces the fixed entry cost of exporting and encourages firms to become exporters. On the other hand, we did not find evidence that information spillovers through main banks have an effect on the export volume or on the growth rate of exports. This is more or less consistent with the findings obtained by Koenig et al. (2010). $ ^{27} $

A key contribution of this paper is that it proposes an additional channel of information spillovers ignored in previous studies. While existing studies, such as Koenig et al. (2010), concentrate on information spillovers from other exporting firms in the same region and/or industry, this study focuses on the importance of information provided directly by main lender banks through transaction relationships. If we look at our results in terms of the argument put forward by Chaney (2008) that a change in fixed costs only affects the extensive margin, while a change in variable costs affects both the intensive and the extensive margin, they suggest that information provided by banks contributes to a reduction in the fixed costs but not in the variable costs associated with exporting. On the other hand, Paravisini et al. (2011) suggest that credit frictions, by affecting the cost of working capital, affect the variable costs of exporting and hence the volume of exports. This result suggests that banks may play an important role in affecting the intensive margin as suppliers of funds. Thus, banks' role as suppliers of funds and as providers of information may affect fixed and variable costs and hence the extensive margin differently. Untangling these two roles of banks and their impact on firms' export behavior is a topic we aim to further address in future research.

This paper also provides an important policy implication. As mentioned in the introduction, our knowledge regarding what factors are important for firms to become an exporter remains very limited, even though export promotion has been an important policy issue in many countries. With regard to Japan, studies such as Wakasugi et al. (2008) and Ito (2011) argue that there are still many firms which do not export even though their performance is good or they actively invest in research and development. Promoting exports by these firms is an urgent policy issue for Japan, which has been facing population decline and sluggish domestic demand for a prolonged period. This paper showed the importance of banks' role as an information provider for potential exporters, implying that the government should proactively involve banks in its export promotion policies. Regional banks – seeing their client firms face declining domestic demand and therefore worried that their own business may shrink – may also be interested in providing more
support services for firms trying to expand their business abroad. Helping such banks to build international service networks and building on the banks' support services may allow the government to implement its export promotion policies more effectively. Moreover, since banks have accumulated a lot of information on their client firms' business, they may have useful knowledge on what type of firms should receive support from the government and on what type of support is most effective. Of course, government and non-profit organizations already provide various support services for firms' international business and for trading companies. Information provided by such organizations or trading companies is complementary to information collected by banks through lending relationships, and it is important for the government to effectively utilize these various information sources for export promotion policies. According to the banker we interviewed, the advantage that banks have is that they possess detailed and wide-ranging information on individual firms' management, financial health, and business activities.

To conclude, we highlight several issues for future research. The first of these concerns the type of information provided by banks. While the information we considered was destination-specific information, it would be possible to take other, more detailed types of information additionally into account, such as industry-specific information. Second, our relatively long-panel dataset allows us to conduct a survival analysis-type of study on the status of exporting firms. This, in turn, allows us to examine how the duration of staying in export markets is determined, which is another important dimension discussed in the theoretical international trade literature (e.g., Schröder and Sørensen 2012). Although there are a fair number of empirical studies analyzing the determinants of the duration of imports, studies on the determinants of what kind of firms are “always” exporters so far have all been only at an aggregate level (e.g., Besedeš and Prusa 2006a, 2006b, Nitsch 2009, Besedeš and Blyde 2010). Third, although the expansion of export destinations, particularly in the case of larger listed firms, often involves the establishment of new subsidiaries or affiliates abroad, this paper, partly because of data constraints, only focused on exporting and did not explicitly deal with foreign direct investment in a new location. As banks provide a wide range of support services for firms which try to open a foreign affiliate, investigating banks’ role in firms’ FDI decision is another promising research topic. Lastly, our results imply that information spillovers through main banks may be more important for smaller firms, which are more likely to choose Asia as their first export destination. Therefore, further investigation focusing on smaller firms would be a worthwhile exercise, if data for small firms were available. We believe that all of these extensions would provide further evidence for a better understanding of firms’ overseas activities and the role of banks.
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